






'''simple docstring'''



import argparse
import random

import joblib
import numpy as np
import torch
from igf.igf import (
    SecondaryLearner,
    collect_objective_set,
    compute_perplexity,
    generate_datasets,
    load_gpta,
    recopy_gpta,
    set_seed,
    train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler

from transformers import GPTaLMHeadModel


def 				lowercase_					(     __A			:							Tuple=3_2       ,   __A			:							Optional[Any]=1_0       ,   __A			:							Optional[int]=1_0_0       ,   __A			:							str=1_0_2_6       ,   __A			:							List[str]=True       ,   __A			:							Dict="data/tokenized_stories_train_wikitext103.jbl"       ,   __A			:							Tuple="igf_context_pairs.jbl"       ,   )							->   Tuple:



 """simple docstring"""




 set_seed(3      )
 # generate train_data and objective_set
 lowercase	,		lowercase       : str						=generate_datasets(
     __A       ,   __A       ,   number=__A       ,   min_len=1_0_2_6       ,   trim=__A      )
 # keeps model same across runs
 set_seed(4      )
 # model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
 # can we train on GPU?
 lowercase       : Optional[Any]						=torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )

 # load pretrained model
 lowercase       : List[str]						=load_gpta('''gpt2'''      ).to(__A      )
 print('''computing perplexity on objective set'''      )
 lowercase       : Tuple						=compute_perplexity(__A       ,   __A       ,   __A      ).item()
 print('''perplexity on objective set:'''       ,   __A      )

 # collect igf pairs and save to file demo.jbl
 collect_objective_set(__A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A      )

 # clean up, delete model and data we don't need anymore
 del model, train_data, objective_set
 torch.cuda.empty_cache()


def 				lowercase_					(     __A			:							Optional[Any]       ,   __A			:							int=1_5       ,   __A			:							Union[str, Any]=1_2_8       ,   __A			:							List[Any]=1_0_0       ,   __A			:							Optional[Any]="igf_model.pt"       ,   )							->   Union[str, Any]:



 """simple docstring"""




 set_seed(4_2      )

 # Load pre-trained model
 lowercase       : List[str]						=GPTaLMHeadModel.from_pretrained('''gpt2'''      )

 # Initialize secondary learner to use embedding weights of model
 lowercase       : str						=SecondaryLearner(__A      )

 # Train secondary learner
 lowercase       : Union[str, Any]						=train_secondary_learner(
     __A       ,   __A       ,   max_epochs=__A       ,   batch_size=__A       ,   eval_freq=1_0_0       ,   igf_model_path=__A       ,   )

 del model, secondary_learner_train_data
 torch.cuda.empty_cache()

 return secondary_learner


def 				lowercase_					(     __A			:							Any       ,   __A			:							Dict       ,   __A			:							List[str]       ,   __A			:							List[str]=3_2       ,   __A			:							Any=1_0_0_0       ,   __A			:							Union[str, Any]=1_6       ,   __A			:							int=1.0       ,   __A			:							Optional[Any]=recopy_gpta       ,   __A			:							Optional[Any]=None       ,   __A			:							Optional[Any]=1_0       ,   __A			:							str="gpt2_finetuned.pt"       ,   )							->   Dict:



 """simple docstring"""




 lowercase       : Tuple						=torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''      )
 lowercase       : Tuple						=RandomSampler(__A      )
 lowercase       : List[str]						=DataLoader(__A       ,   sampler=__A      )

 lowercase       : List[str]						=max_steps // (len(__A      )) + 1
 lowercase       : List[Any]						=0
 lowercase       : Any						=torch.zeros((1, context_len)       ,   dtype=torch.long       ,   device=__A      )
 lowercase	,		lowercase	,		lowercase       : Union[str, Any]						=recopy_model(__A       ,   __A       ,   __A      )

 model.train()
 if secondary_learner is not None:
  secondary_learner.to(__A      )
  secondary_learner.eval()
 lowercase       : int						=[]
 lowercase       : List[Any]						=0

 lowercase       : Optional[int]						=[]
 lowercase       : Any						=[]

 # Compute the performance of the transformer model at the beginning
 lowercase       : str						=compute_perplexity(__A       ,   __A       ,   __A      )
 test_perps.append(__A      )
 print('''Test perplexity, step'''       ,   __A       ,   ''':'''       ,   __A      )
 for epoch in range(int(__A      )      ):
  for step, example in enumerate(__A      ):
   torch.cuda.empty_cache()
   lowercase       : int						=random.randint(0       ,   example.size(2      ) - context_len - 1      )
   lowercase       : Optional[int]						=example[0, 0, start : start + context_len]
   lm_optimizer.zero_grad()
   lowercase       : int						=model(__A       ,   labels=__A      )
   lowercase       : List[str]						=True

   if secondary_learner is not None:
    lowercase       : str						=secondary_learner.forward(
        torch.tensor(__A       ,   dtype=torch.long       ,   device=__A      ).unsqueeze(0      )      )[0].item()
    observed_qs.append(float(__A      )      )

    # Here we implement the simple non-constant threshold for the predicted IG(X) value
    # We will decay the selectivity of our secondary learner filter from
    # 1 standard deviation above average to 1 below average after 10 batches.

    if global_step == 1_0:
     lowercase       : List[str]						=-1
    if predicted_q < threshold:
     lowercase       : Union[str, Any]						=False

            # If we passed the filter, add the context to the batch!
   if do_backprop:
    contexts.append(np.array(context.cpu()      )      )
    lowercase       : List[Any]						=outputs[0]
    lm_loss.backward()
    examples += 1

   del outputs

   # Once the batch is filled with enough contexts, backprop on the batch.
   if examples == batch_size:
    torch.cuda.empty_cache()
    lowercase       : Any						=0
    # Do LM backprop
    torch.nn.utils.clip_grad_norm_(model.parameters()       ,   3.0      )
    lm_optimizer.step()
    lm_scheduler.step()  # Update learning rate schedule
    global_step += 1
    # Compute the performance of the transformer model at this batch
    if global_step % eval_interval == 0:
     lowercase       : List[Any]						=compute_perplexity(__A       ,   __A       ,   __A      )
     test_perps.append(__A      )

     print('''Test perplexity, step'''       ,   __A       ,   ''':'''       ,   __A      )
            # Break out of the loop after 60 batches
   if max_steps > 0 and global_step > 6_0:
    break
  if max_steps > 0 and global_step > 6_0:
   break

    # save finetuned transformer model
 torch.save(model.state_dict()       ,   __A      )
 torch.cuda.empty_cache()
 # Do some cleaning up so we can reinitialize for the next run of this function
 del lm_optimizer
 del lm_scheduler
 return model


def 				lowercase_					(     )							->   int:



 """simple docstring"""




 lowercase       : List[str]						=argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task'''      )

 # Required parameters
 parser.add_argument(
     '''--data_dir'''       ,   default=__A       ,   type=__A       ,   required=__A       ,   help='''The input data dir. Should contain data files for WikiText.'''       ,   )
 parser.add_argument(
     '''--model_name_or_path'''       ,   default=__A       ,   type=__A       ,   required=__A       ,   help='''Path to pretrained model or model identifier from huggingface.co/models'''       ,   )
 parser.add_argument(
     '''--data_file'''       ,   type=__A       ,   default=__A       ,   help=(
         '''A jbl file containing tokenized data which can be split as objective dataset, '''
         '''train_dataset and test_dataset.'''
     )       ,   )

 parser.add_argument(
     '''--igf_data_file'''       ,   type=__A       ,   default=__A       ,   help='''A jbl file containing the context and information gain pairs to train secondary learner.'''       ,   )

 parser.add_argument(
     '''--output_dir'''       ,   default=__A       ,   type=__A       ,   required=__A       ,   help='''The output directory where the final fine-tuned model is stored.'''       ,   )

 parser.add_argument(
     '''--tokenizer_name'''       ,   default=__A       ,   type=__A       ,   help='''Pretrained tokenizer name or path if not the same as model_name'''       ,   )
 parser.add_argument('''--seed'''       ,   type=__A       ,   default=__A       ,   help='''A seed for reproducible training.'''      )

 parser.add_argument(
     '''--context_len'''       ,   default=3_2       ,   type=__A       ,   help=(
         '''The maximum total input sequence length after tokenization. Sequences longer '''
         '''than this will be truncated, sequences shorter will be padded.'''
     )       ,   )

 parser.add_argument(
     '''--size_objective_set'''       ,   default=1_0_0       ,   type=__A       ,   help='''number of articles that are long enough to be used as our objective set'''       ,   )
 parser.add_argument(
     '''--eval_freq'''       ,   default=1_0_0       ,   type=__A       ,   help='''secondary model evaluation is triggered at eval_freq'''      )

 parser.add_argument('''--max_steps'''       ,   default=1_0_0_0       ,   type=__A       ,   help='''To calculate training epochs'''      )

 parser.add_argument(
     '''--secondary_learner_batch_size'''       ,   default=1_2_8       ,   type=__A       ,   help='''batch size of training data for secondary learner'''       ,   )

 parser.add_argument(
     '''--batch_size'''       ,   default=1_6       ,   type=__A       ,   help='''batch size of training data of language model(gpt2) '''      )

 parser.add_argument(
     '''--eval_interval'''       ,   default=1_0       ,   type=__A       ,   help=(
         '''decay the selectivity of our secondary learner filter from'''
         '''1 standard deviation above average to 1 below average after 10 batches'''
     )       ,   )

 parser.add_argument(
     '''--number'''       ,   default=1_0_0       ,   type=__A       ,   help='''The number of examples split to be used as objective_set/test_data'''      )

 parser.add_argument(
     '''--min_len'''       ,   default=1_0_2_6       ,   type=__A       ,   help='''The minimum length of the article to be used as objective set'''      )

 parser.add_argument(
     '''--secondary_learner_max_epochs'''       ,   default=1_5       ,   type=__A       ,   help='''number of epochs to train secondary learner'''      )

 parser.add_argument('''--trim'''       ,   default=__A       ,   type=__A       ,   help='''truncate the example if it exceeds context length'''      )

 parser.add_argument(
     '''--threshold'''       ,   default=1.0       ,   type=__A       ,   help=(
         '''The threshold value used by secondary learner to filter the train_data and allow only'''
         ''' informative data as input to the model'''
     )       ,   )

 parser.add_argument('''--finetuned_model_name'''       ,   default='''gpt2_finetuned.pt'''       ,   type=__A       ,   help='''finetuned_model_name'''      )

 parser.add_argument(
     '''--recopy_model'''       ,   default=__A       ,   type=__A       ,   help='''Reset the model to the original pretrained GPT-2 weights after each iteration'''       ,   )

 # function calls
 # Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
 generate_n_pairs(
     context_len=3_2       ,   max_steps=1_0       ,   size_objective_set=1_0_0       ,   min_len=1_0_2_6       ,   trim=__A       ,   data_file='''data/tokenized_stories_train_wikitext103.jbl'''       ,   igf_data_file='''igf_context_pairs.jbl'''       ,   )

 # Load train data for secondary learner
 lowercase       : Optional[int]						=joblib.load('''data/IGF_values.jbl'''      )

 # Train secondary learner
 lowercase       : str						=training_secondary_learner(
     __A       ,   secondary_learner_max_epochs=1_5       ,   secondary_learner_batch_size=1_2_8       ,   eval_freq=1_0_0       ,   igf_model_path='''igf_model.pt'''       ,   )

 # load pretrained gpt2 model
 lowercase       : Any						=GPTaLMHeadModel.from_pretrained('''gpt2'''      )
 set_seed(4_2      )

 # Generate train and test data to train and evaluate gpt2 model
 lowercase	,		lowercase       : List[Any]						=generate_datasets(
     context_len=3_2       ,   file='''data/tokenized_stories_train_wikitext103.jbl'''       ,   number=1_0_0       ,   min_len=1_0_2_6       ,   trim=__A      )

 # fine-tuning of the gpt2 model using igf (Information Gain Filtration)
 finetune(
     __A       ,   __A       ,   __A       ,   context_len=3_2       ,   max_steps=1_0_0_0       ,   batch_size=1_6       ,   threshold=1.0       ,   recopy_model=__A       ,   secondary_learner=__A       ,   eval_interval=1_0       ,   finetuned_model_name='''gpt2_finetuned.pt'''       ,   )


if __name__ == "__main__":
      main()







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


_SCREAMING_SNAKE_CASE     :					Optional[int]      =       {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       ["SpeechEncoderDecoderModel"]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _SCREAMING_SNAKE_CASE     :					Dict      =       ["FlaxSpeechEncoderDecoderModel"]

if TYPE_CHECKING:
 from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel

else:
 import sys

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




